Low bit-width Quantized Neural Networks (QNNs) enable deployment of complex machine
learning models on constrained devices such as microcontrollers (MCUs) by reducing their …

IoT end-nodes require extreme performance and energy efficiency coupled with high
flexibility to deal with the increasing computational requirements and variety of modern near …

The world has seen the great success of deep neural networks (DNNs) in a massive number
of artificial intelligence (AI) applications. However, develo** high-quality AI services to …

Quantization for Convolutional Neural Network (CNN) has shown significant progress with
the intention of reducing the cost of computation and storage with low-bitwidth data inputs …

Modern scientific instruments produce vast amounts of data, which can overwhelm the
processing ability of computer systems. Lossy compression of data is an intriguing solution …

Recent years have seen a rapid emergence of manycore machines and multicore
processors with twenty or more cores. While writing code for the standard CPUs requires …

A new generation of manycore processors is on the rise that offers dozens and more cores
on a chip and, in a sense, fuses host processor and accelerator. In this paper we target the …

Develo** high-performance code for numerical domains is challenging, as it requires
hand-in-hand specialization with the continuous evolution of modern hardware. Program …